#Influence

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What it does

#Influence is a web application to find out influencers behind
a Twitter hashtag and to visualize how powerful they are. By using intuitively
easy-to-understand “bubble data visualization,” it shows top 5 influential
people on a certain hashtag as a circle, and it even displays top retweeters
for the influencer within the circle. Besides, the size of circles represents
the size of influence.

In today’s Twitter Sphere, we see a great number of hashtags
being produced, mentioned and shared every second, and we use them with little
awareness of the origin. So there are fair questions to ask—who are the
invisible hands behind the viral hashtags and how big are their influences?

As finding influencers is becoming important for digital
marketing, established services such as official Twitter or Keyhole begun to
provide information about influencers. But it’s sometimes based on closed
algorithm or offered in the form of complex statistics. Developing #influence,
we wanted to demystify the collective concept gathered under the hashtags in a
more democratic and understandable way than any other service does.

How it works

Each time a user types in a hashtag in a search box at the
first page, the Twitter API returns the 100 most
popular tweets about the hashtag at that moment
based on Twitter's own algorithm. D3.js
sorts the tweets out based on the number of retweets and renders the top 5
tweets as big circles. The size of circle represents the number of retweets in
the first level visualization.

For each top tweet, D3.js ranks its
retweeters by looking at their follower numbers, and displays up to the top 10
retweeters as the small circles within a big circles. Again, in the
second level visualization, unlike the 5 circles in the first level, the size
of circle is based on the follower numbers.

We acknowledge that our method is
far from perfect, for two main reasons: First, Twitter's API doesn't count
edited retweets, so those retweets don't show up in our visualization. Second,
Twitter's API doesn't track the retweets of a retweet, so we evaluate the
influence of the retweeters (second level circles) based on their follower
numbers instead.

Key Technologies

●Twitter API via twit client for Node.js

●D3.js

●Express for Node.js

●HTML

●CSS

Next Steps

When considering future work with #Influence, we should work on
current limitations in the system. A central problem with this product is the
lack of robustness to multiple simultaneous users. To address this, we need to
implement a new system for passing Twitter data to the frontend that does not
require individual clients to share data resources from the server.

We should also optimize the system’s performance by redesigning
the Twitter API queries to call each other recursively. This would allow the
page to return a result in the minimum amount of time. We might also be able to
improve the system’s performance by storing retweeter IDs in database to avoid
repeated lookup queries to Twitter. Over time, frequently influential
retweeters would no longer need to be looked up.

An additional interesting feature that could be added to
#Influence would involve parsing Twitter account descriptions to discover
users’ areas of expertise. This would allow the user to compare an individual
user’s influence in a particular topic with their authority on it.